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Large Language Models for Automated Cross-Domain Machine Learning Task Type Identification: A Benchmark Dataset and Evaluation — arXiv:2609.35335

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.35335
License (as archived): CC0-1.0
Archived: 2026-10-07T21:03:28.113Z
SHA-256 of the archived text: 7058d18f39e1d1578ace8462d787dc1d104fd257d22de6ad5f2649dd8d96214d
Stable URL of this page: https://metraipolis.com/documento/dc_bacc449afe45

How to cite

Large Language Models for Automated Cross-Domain Machine Learning Task Type Identification: A Benchmark Dataset and Evaluation — arXiv:2609.35335 — arXiv (en), https://arxiv.org/abs/2609.35335 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bacc449afe45 (Metraipolis archive dc_bacc449afe45, sha256 7058d18f39e1d1578ace8462d787dc1d104fd257d22de6ad5f2649dd8d96214d)

BibTeX

@misc{dcbacc449afe45,
  title = {Large Language Models for Automated Cross-Domain Machine Learning Task Type Identification: A Benchmark Dataset and Evaluation — arXiv:2609.35335},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.35335},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bacc449afe45; sha256 7058d18f39e1d1578ace8462d787dc1d104fd257d22de6ad5f2649dd8d96214d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.35335. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Machine learning task type identification is essential for constructing valid ML pipelines, yet in practice it is typically specified manually. We investigate whether large language models (LLMs) can infer both the data domain and the downstream prediction task directly from dataset-level information when only the target feature is provided by the user. Together with our LLM-based system we also release an annotated benchmark comprising 625 public tabular and time series datasets. We evaluate the proposed approach in three settings: (i) tabular datasets in comparison with established AutoML heuristics, (ii) cross-domain evaluation across tabular and time series datasets, and (iii) a practical deployment scenario using smaller local models. The results show consistent advantages for LLM-based task type identification, with increasing difficulty in heterogeneous and resource-constrained settings. LLM-based approaches outperform AutoGluon in the tabular setting, reaching 0.98 F1 macro compared to 0.93. In the cross-domain setting, the best model achieves 0.90 F1 macro, while smaller locally deployable models reach 0.75, indicating a trade-off between deployment feasibility and accuracy.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.35335 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T20:29:03.318Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).